Non-linear modelling and forecasting of S&P 500 volatility

被引：10

作者：

Verhoeven, P ^{[1
]}

Pilgram, B

McAleer, M

Mees, A

机构：

[1] Curtin Univ Technol, Sch Econ & Finance, Perth, WA 6102, Australia

[2] Univ Western Australia, Dept Math & Stat, Perth, WA 6907, Australia

[3] Univ Western Australia, Dept Econ, Perth, WA 6907, Australia

来源：

MATHEMATICS AND COMPUTERS IN SIMULATION | 2002年 / 59卷 / 1-3期

关键词：

non-linear Markov modelling; non-parametric model; parametric model; volatility forecasting;

D O I：

10.1016/S0378-4754(01)00411-6

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper investigates the use of a flexible forecasting method based on non-linear Markov modelling and canonical variate analysis, and the use of a prediction algorithm to forecast conditional volatility. We assess the dynamic behaviour of the model by forecasting volatility of a stock index. It is found that the non-linear non-parametric model based on canonical variate analysis forecasts stock index volatility significantly better than the GJR-GARCH(1, 1)-t model due to the flexibility in accommodating multiple dynamic patterns in volatility which are not captured by its parametric counterpart. (C) 2002 IMACS. Published by Elsevier Science B.V. All fights reserved.

引用

页码：233 / 241

页数：9

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